Robust analysis of corporate balance sheets and fixed income instruments requires solid fact-based inputs regarding tariff policy. Few experts were surprised when the new Trump Administration entered office ready, willing, and able to impose tariffs quickly. But the government's use of emergency economic powers (see THIS analysis) changed the landscape of tariff policy for the foreseeable future.
These are just the opening days of a significant geopolitical rebalancing as policymakers in the major advanced economies redefine their terms of engagement amongst each other and their trade partner countries. The macro trend was predictable.
But did you know that a broad range of strategically significant moves occurred in January long before President Trump took office that set the stage for the currently benign tariff policy cycle? Were you able to spot the key policy shifts that failed to attract media attention during January that materially impacted how the tariff situation worked out in early February?
BCMstrategy, Inc.'s macroVS product includes data generated from the policy process across the following key issue areas:
trade/tariff policy,
monetary policy,
supply chain disruption policy, and
critical minerals (all of them).
See how our patented PolicyScope process provided predictive signals regarding tariff geopolitical positioning in January 2025. Learn how to spot tariff policy trends in three steps.
How To Spot Tariff Policy Trends in Three Steps
Step 1: Daily Volatility and Momentum Measurements
PolicyScope data covers many policymakers. The chart below selected six significant jurisdictions to compare activity levels across time and across all the issues for which we generate data. Can you spot the anomaly?

The most active sovereign on a simple notional amount basis BEFORE the Inauguration was the United States.
The sheer volume and velocity of activity from the outgoing Biden Administration outpaced all other advanced economies. AFTER the Inauguration, the most active sovereign on a simple notional amount basis was the European Union. High volume levels, send a strong signal that policy is on the move.
So what did you miss amid the policy news cycle? Here are some selected strategic moves that occurred in January:
European Union Expands its Global Footprint: the EU/Japan Strategic Partnership went into effect, free trade negotiations with MALAYSIA were reinstated, a Strategic Partnership with MEXICO was finalized.
European Union Tariffs (China): The EU extended for another 5 years its tariffs on Chinese electric bicycles.
European Union Tariffs (Belarus and Russia): The EU proposed new tariffs on agricultural imports from Belarus and Russia as a punishment for the ongoing war in Ukraine.
United States Tariff Report (China): The Biden Administration submitted its report to Congress detailing all the ways in which China continued to flout its WTO commitments.

This knowledge would support the following fact-based insights:
The EU is strategically building buffer zones to insulate certain sovereigns from US tariff downdrafts.
The EU is imposing tariffs to support regional security and geostrategic goals.
The United States (and the incoming Trump Administration) had most of the documentary evidence required to start a trade war with China using traditional trade tools like Section 301 and Section 232.
All of that would be possible from a quick look at the time series BEFORE applying any advanced analytics or ML/AI processes on the quantitative data much less the underlying language itself (which we also store in structured and unstructured formats).
Step 2: Identify Relationships
We use the patented PolicyScope data to generate a wide range of ML/AI training data. Some of that data delivers weighted and unweighted relationships among actors and issues.
You can compare relative activity levels across all thematic verticals for which we currently generate data (e.g., Digital Currency Policy, Climate and Energy Policy, Monetary Policy--which includes Trade Policy issues). If you were to ask the data for the three most active issues during January 2025, you would receive this information:

The data supports a wide range of differential analysis using the quantification generated from our patented process.
The benefits to predictive analytics extend well beyond quantitative volatility measurement and trend projection. Consider how much more efficient and accurate your generativeAI solutions would be if your training data already provided this level of foundation training:

Step 3: Generate Signals and Reports -- Use Cases
Signal generation varies by user. We work with customers to configure optimal signals based on their specific deployment within
portfolio management and FX trading,
investment management,
insurance,
policy strategists, and
government relations.
We also generate customized embeddings and language training to support generative AI automated research assistants that can provide document summaries and more.
BCMstrategy, Inc. generates AI training data from the language of public policy. The training data support both quantitative policy trend projection and generative AI solutions at client companies using an award-winning, patented process.